On Feb 15, 2025, at 7:58 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
Dear John,
Our workflow for an open and reproducible workflow is to publish the data
via Zenodo. https://zenodo.org/ is maintained by CERN.
- The data is freely available.
- Your data is easy to cite.
- Every version gets its own DOI + one stable DOI that always points to the
most recent version. E.g. https://doi.org/10.5281/zenodo.14179531
The zen4R package makes it easy to upload and download the data from within
R. Our functions assume the data is in a local folder. Only when the data
is missing, we try to download it from Zenodo.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
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Op vr 14 feb 2025 om 15:55 schreef John Clarke <
john.clarke at cornerstonenw.com>:
Hi folks,
I've looked around for this particular question, but haven't found a good
answer. I have a versioned dataset that includes about 6 csv files that
total about 15MB for each version. The versions get updated every few years
or so and are used to drive the model which was written in C++ but is now
inside an Rcpp wrapper. Apart from the fact that CRAN does not permit large
files, I want to have a better way for users to access particular versions
of the dataset.
Usage idea:
# The following would hopefully also download default/most recent version
of the csv files from CRAN (if allowed) or Github or some other repository
for academic open source data.
install.packages("MyPackage")
mypackage = new(MyPackage)
Then, if necessary, the user could change the dataset used with something
like:
mypackage.dataset("2.1.0") which would retrieve new csv files if they
haven't already been downloaded and update the data_folder path internally
to point to 2.1.0 directory.
Requirements:
- The dataset is csv (not a R data object) and the Rcpp MyPackage expects
this format
- Would be nice to properly include citations for the data as they will
likely be initially released through a journal publication
What is the best practice for this sort of dataset management for a package
in R? Is it okay to use Github to store and version the data? Or
preferred to use an R package (ignoring the file size limit). Or some other
open source data hosting? I see https://r-universe.dev/ as an option as
well. In any case, what is the proper mechanism for retrieving/caching the
data?
Thanks,
-John
John Clarke | Senior Technical Advisor |
Cornerstone Systems Northwest | john.clarke at cornerstonenw.com
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